Geographic ratemaking with spatial embeddings

• URL: http://arxiv.org/abs/2104.12852v1
• Date: Mon, 26 Apr 2021 20:09:45 GMT
• Title: Geographic ratemaking with spatial embeddings
• Authors: Christopher Blier-Wong and H\'el\`ene Cossette and Luc Lamontagne and Etienne Marceau
• Abstract summary: Insurance companies with high exposures in a territory typically have a competitive advantage. Relying on geographic losses is problematic for areas where past loss data is unavailable. This paper presents a method based on data (instead of smoothing historical insurance claim losses) to construct a geographic ratemaking model.
• Score: 0.3211619859724084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatial data is a rich source of information for actuarial applications: knowledge of a risk's location could improve an insurance company's ratemaking, reserving or risk management processes. Insurance companies with high exposures in a territory typically have a competitive advantage since they may use historical losses in a region to model spatial risk non-parametrically. Relying on geographic losses is problematic for areas where past loss data is unavailable. This paper presents a method based on data (instead of smoothing historical insurance claim losses) to construct a geographic ratemaking model. In particular, we construct spatial features within a complex representation model, then use the features as inputs to a simpler predictive model (like a generalized linear model). Our approach generates predictions with smaller bias and smaller variance than other spatial interpolation models such as bivariate splines in most situations. This method also enables us to generate rates in territories with no historical experience.

Related papers

• Rejection via Learning Density Ratios [50.91522897152437]
  Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions. We propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance. Our framework is tested empirically over clean and noisy datasets.
  arXiv  Detail & Related papers  (2024-05-29T01:32:17Z)
• GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image [94.56927147492738]
  We introduce GeoWizard, a new generative foundation model designed for estimating geometric attributes from single images. We show that leveraging diffusion priors can markedly improve generalization, detail preservation, and efficiency in resource usage. We propose a simple yet effective strategy to segregate the complex data distribution of various scenes into distinct sub-distributions.
  arXiv  Detail & Related papers  (2024-03-18T17:50:41Z)
• Geospatial Disparities: A Case Study on Real Estate Prices in Paris [0.3495246564946556]
  We propose a toolkit for identifying and mitigating biases arising from geospatial data. We incorporate an ordinal regression case with spatial attributes, deviating from the binary classification focus. Illustrating our methodology, we showcase practical applications and scrutinize the implications of choosing geographical aggregation levels for fairness and calibration measures.
  arXiv  Detail & Related papers  (2024-01-29T14:53:14Z)
• Spatial-temporal Forecasting for Regions without Observations [13.805203053973772]
  We study spatial-temporal forecasting for a region of interest without any historical observations. We propose a model named STSM for the task. Our key insight is to learn from the locations that resemble those in the region of interest.
  arXiv  Detail & Related papers  (2024-01-19T06:26:05Z)
• SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data [55.609946936979036]
  We propose a novel spatial event ranking approach named SpatialRank. We show that SpatialRank can effectively identify the top riskiest locations of crimes and traffic accidents.
  arXiv  Detail & Related papers  (2023-09-30T06:20:21Z)
• SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation [62.889835139583965]
  We introduce an unsupervised auxiliary task of learning an implicit underlying surface representation simultaneously on source and target data. As both domains share the same latent representation, the model is forced to accommodate discrepancies between the two sources of data. Our experiments demonstrate that our method achieves a better performance than the current state of the art, both in real-to-real and synthetic-to-real scenarios.
  arXiv  Detail & Related papers  (2023-04-06T17:36:23Z)
• HintNet: Hierarchical Knowledge Transfer Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal Data [17.345649325770957]
  Traffic accident forecasting is a significant problem for transportation management and public safety. The occurrence of traffic accidents is affected by complex dependencies among spatial and temporal features. Recent traffic accident prediction methods have attempted to use deep learning models to improve accuracy.
  arXiv  Detail & Related papers  (2022-03-07T02:11:04Z)
• Approximate Regions of Attraction in Learning with Decision-Dependent Distributions [11.304363655760513]
  We analyze repeated risk minimization as the trajectories of the gradient flows of performative risk minimization. We provide conditions to characterize the region of attraction for the various equilibria in this setting. We introduce the notion of performative alignment, which provides a geometric condition on the convergence of repeated risk minimization to performative risk minimizers.
  arXiv  Detail & Related papers  (2021-06-30T18:38:08Z)
• Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems [0.7340017786387767]
  In this paper, we describe our pipeline that uses data related to roadway geometry, weather, historical accidents, and real-time traffic congestion to aid accident forecasting. Experimental results show that our approach can significantly reduce response times in the field in comparison with current approaches.
  arXiv  Detail & Related papers  (2021-06-15T17:33:36Z)
• Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning [57.88785630755165]
  Empirical risk minimization (ERM) is the workhorse of machine learning, but its model-agnostic guarantees can fail when we use adaptively collected data. We study a generic importance sampling weighted ERM algorithm for using adaptively collected data to minimize the average of a loss function over a hypothesis class. For policy learning, we provide rate-optimal regret guarantees that close an open gap in the existing literature whenever exploration decays to zero.
  arXiv  Detail & Related papers  (2021-06-03T09:50:13Z)
• Graph Embedding with Data Uncertainty [113.39838145450007]
  spectral-based subspace learning is a common data preprocessing step in many machine learning pipelines. Most subspace learning methods do not take into consideration possible measurement inaccuracies or artifacts that can lead to data with high uncertainty.
  arXiv  Detail & Related papers  (2020-09-01T15:08:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.